2022-07-11

Interactions

Included below are the models with adi standardized and education updated to be the average of both (more details in that section). Note that each plot is associated with the table below it.

A few things I’ll quickly note about the plots:

  1. A 1 indicates preterm, while a 0 indicates term

  2. The vertical distance between the lines at zero on the x-axis is the baseline difference in thickness between term and preterm

  3. The slopes of the line demonstrate the effect of SES. If the line is flat, for example, SES metric has little explantory power. A good example of this is ADI in the inferior frontal gyrus. For term children, this line is flat

  4. Following off of (3), differences in slope are associated with differences in the interaction term. Interesting cases are those instances in which the lines cross, or in which one line is flat and the other is not

  5. Finally, in addition to each line I have included a second plot that shows the distribution of participants superimposed on the plot. From this, we see

    1. The absolute difference between the lines is often small, relative to the observed variability of subjects. This is particularly true with smaller effect sizes
    2. There tend to be clusters of values on the x-axis. For example, income-to-needs tends to be mostly below 3, ADI is largely between -1 and 1 (as we would expect). Education is a bit trickier to see, so I included a table of paired values

Summaries

These models are for the analysis for hypothesis 1 and 2. That is, linear mixed effects models with ROI thickness as dependent, and age, sex, race/ethnicity, SES, term/pretern, and interaction as independent. Random intercepts for site and laterality.

Each tab is one of the SES metrics, containing model summaries for all brain regions

ADI

Plot of standardized ADI by preterm status

destrieux_g.front.inf.opercular
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.1339 0.0284 3.0783 3.1895 0.0000
race_ethnicityBlack 0.0641 0.0134 0.0378 0.0904 0.0000
race_ethnicityHispanic 0.0199 0.0134 -0.0063 0.0461 0.1364
race_ethnicityOther 0.0311 0.0133 0.0049 0.0572 0.0198
race_ethnicityWhite 0.0304 0.0129 0.0051 0.0558 0.0188
femaleyes 0.0215 0.0026 0.0165 0.0265 0.0000
premature -0.0002 0.0035 -0.0069 0.0066 0.9630
adi -0.0036 0.0017 -0.0070 -0.0002 0.0355
age -0.0006 0.0002 -0.0010 -0.0003 0.0002
premature:adi 0.0106 0.0034 0.0039 0.0173 0.0020

destrieux_g.front.inf.orbital
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.2870 0.0425 3.2036 3.3703 0.0000
race_ethnicityBlack 0.0376 0.0197 -0.0009 0.0762 0.0556
race_ethnicityHispanic 0.0347 0.0196 -0.0036 0.0731 0.0760
race_ethnicityOther 0.0481 0.0195 0.0099 0.0863 0.0137
race_ethnicityWhite 0.0625 0.0189 0.0254 0.0996 0.0010
femaleyes 0.0276 0.0038 0.0203 0.0350 0.0000
premature -0.0156 0.0051 -0.0255 -0.0056 0.0021
adi -0.0094 0.0025 -0.0142 -0.0045 0.0002
age -0.0013 0.0003 -0.0018 -0.0008 0.0000
premature:adi 0.0069 0.0050 -0.0029 0.0167 0.1675

destrieux_g.front.inf.triangul
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.1254 0.0369 3.0532 3.1977 0.0000
race_ethnicityBlack 0.0335 0.0157 0.0027 0.0644 0.0330
race_ethnicityHispanic 0.0170 0.0157 -0.0137 0.0477 0.2782
race_ethnicityOther 0.0272 0.0156 -0.0034 0.0579 0.0810
race_ethnicityWhite 0.0338 0.0152 0.0041 0.0635 0.0258
femaleyes 0.0318 0.0030 0.0260 0.0377 0.0000
premature -0.0090 0.0041 -0.0169 -0.0010 0.0271
adi -0.0009 0.0020 -0.0049 0.0030 0.6418
age -0.0012 0.0002 -0.0016 -0.0008 0.0000
premature:adi 0.0106 0.0040 0.0027 0.0184 0.0084

destrieux_g.front.middle
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.0509 0.0366 2.9792 3.1226 0.0000
race_ethnicityBlack 0.0423 0.0135 0.0158 0.0688 0.0018
race_ethnicityHispanic 0.0194 0.0135 -0.0070 0.0458 0.1505
race_ethnicityOther 0.0315 0.0134 0.0052 0.0579 0.0189
race_ethnicityWhite 0.0345 0.0130 0.0089 0.0600 0.0083
femaleyes 0.0311 0.0026 0.0261 0.0362 0.0000
premature -0.0132 0.0035 -0.0200 -0.0064 0.0002
adi -0.0013 0.0017 -0.0047 0.0021 0.4505
age -0.0010 0.0002 -0.0014 -0.0007 0.0000
premature:adi 0.0108 0.0034 0.0040 0.0175 0.0018

destrieux_g.pariet.inf.supramar
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.0432 0.0454 2.9543 3.1322 0.0000
race_ethnicityBlack 0.0248 0.0140 -0.0026 0.0522 0.0762
race_ethnicityHispanic 0.0438 0.0139 0.0165 0.0710 0.0017
race_ethnicityOther 0.0392 0.0139 0.0120 0.0664 0.0047
race_ethnicityWhite 0.0587 0.0135 0.0323 0.0851 0.0000
femaleyes 0.0305 0.0027 0.0253 0.0357 0.0000
premature -0.0048 0.0036 -0.0118 0.0023 0.1861
adi -0.0034 0.0018 -0.0069 0.0001 0.0555
age -0.0008 0.0002 -0.0011 -0.0004 0.0000
premature:adi 0.0123 0.0036 0.0053 0.0193 0.0006

destrieux_g.temporal.middle
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.3745 0.0447 3.2870 3.4620 0.0000
race_ethnicityBlack 0.0205 0.0160 -0.0109 0.0519 0.2004
race_ethnicityHispanic 0.0598 0.0160 0.0285 0.0911 0.0002
race_ethnicityOther 0.0626 0.0159 0.0314 0.0938 0.0001
race_ethnicityWhite 0.0901 0.0155 0.0598 0.1204 0.0000
femaleyes 0.0152 0.0031 0.0092 0.0212 0.0000
premature -0.0129 0.0041 -0.0210 -0.0048 0.0018
adi -0.0068 0.0021 -0.0109 -0.0028 0.0009
age -0.0008 0.0002 -0.0012 -0.0004 0.0002
premature:adi 0.0139 0.0041 0.0059 0.0219 0.0007

destrieux_g.temp.sup.lateral
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.2907 0.0440 3.2044 3.3769 0.0000
race_ethnicityBlack 0.0534 0.0179 0.0183 0.0885 0.0029
race_ethnicityHispanic 0.0391 0.0178 0.0041 0.0740 0.0285
race_ethnicityOther 0.0571 0.0178 0.0223 0.0919 0.0013
race_ethnicityWhite 0.0766 0.0173 0.0427 0.1104 0.0000
femaleyes 0.0354 0.0034 0.0287 0.0421 0.0000
premature 0.0083 0.0046 -0.0007 0.0173 0.0723
adi -0.0093 0.0023 -0.0138 -0.0048 0.0001
age 0.0000 0.0002 -0.0005 0.0004 0.9569
premature:adi 0.0209 0.0046 0.0119 0.0298 0.0000

destrieux_s.temporal.transverse
Estimate Std. Error 95%L 95%U p-val
(Intercept) 2.8189 0.0810 2.6602 2.9776 0.0006
race_ethnicityBlack -0.0331 0.0245 -0.0810 0.0149 0.1763
race_ethnicityHispanic -0.0140 0.0244 -0.0617 0.0338 0.5661
race_ethnicityOther -0.0051 0.0243 -0.0527 0.0426 0.8347
race_ethnicityWhite 0.0061 0.0236 -0.0401 0.0523 0.7961
femaleyes 0.0713 0.0047 0.0621 0.0805 0.0000
premature -0.0048 0.0063 -0.0171 0.0076 0.4493
adi -0.0079 0.0030 -0.0138 -0.0019 0.0096
age -0.0012 0.0003 -0.0018 -0.0006 0.0001
premature:adi 0.0116 0.0063 -0.0007 0.0239 0.0635

Parent Education

For this, I translated each education level into either

  1. < HS
  2. HS
  3. Some college
  4. Bachelor
  5. Post graduate

where each value corresponds to the associated number in the list. I think took the average numeric between parent and partner, so these are treated as a numeric variable between 1 and 5 as well. A table of pairings and distribution of values is here (extra large to fit labels):

< HS Diploma Bachelor HS Diploma/GED Post Graduate Degree Some College
< HS Diploma 225 315 322 242 750
Bachelor 1 991 32 708 330
HS Diploma/GED 57 159 223 105 386
Post Graduate Degree 3 640 16 967 167
Some College 26 472 129 294 797

Alternatively, we could add the numbers together, giving us a range of 2-10, which we could treat as ordered categorical variables. Something to think on

destrieux_g.front.inf.opercular
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.1215 0.0290 3.0647 3.1783 0.0000
race_ethnicityBlack 0.0652 0.0135 0.0387 0.0918 0.0000
race_ethnicityHispanic 0.0204 0.0134 -0.0059 0.0467 0.1280
race_ethnicityOther 0.0311 0.0133 0.0049 0.0572 0.0199
race_ethnicityWhite 0.0301 0.0129 0.0047 0.0555 0.0201
femaleyes 0.0217 0.0026 0.0166 0.0267 0.0000
premature 0.0188 0.0110 -0.0028 0.0405 0.0883
ed_avg_numeric 0.0031 0.0014 0.0002 0.0059 0.0344
age -0.0006 0.0002 -0.0010 -0.0003 0.0003
premature:ed_avg_numeric -0.0055 0.0031 -0.0116 0.0006 0.0750

destrieux_g.front.inf.orbital
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.2640 0.0434 3.1789 3.3490 0.0000
race_ethnicityBlack 0.0388 0.0198 0.0000 0.0776 0.0498
race_ethnicityHispanic 0.0354 0.0196 -0.0031 0.0739 0.0718
race_ethnicityOther 0.0481 0.0195 0.0098 0.0863 0.0139
race_ethnicityWhite 0.0624 0.0189 0.0252 0.0995 0.0010
femaleyes 0.0277 0.0038 0.0204 0.0351 0.0000
premature 0.0006 0.0162 -0.0311 0.0323 0.9718
ed_avg_numeric 0.0060 0.0021 0.0018 0.0101 0.0049
age -0.0013 0.0003 -0.0018 -0.0008 0.0000
premature:ed_avg_numeric -0.0047 0.0046 -0.0137 0.0042 0.3004

destrieux_g.front.inf.triangul
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.1210 0.0376 3.0474 3.1946 0.0000
race_ethnicityBlack 0.0340 0.0159 0.0029 0.0650 0.0321
race_ethnicityHispanic 0.0171 0.0157 -0.0137 0.0479 0.2769
race_ethnicityOther 0.0270 0.0156 -0.0037 0.0576 0.0845
race_ethnicityWhite 0.0331 0.0152 0.0034 0.0629 0.0290
femaleyes 0.0320 0.0030 0.0261 0.0378 0.0000
premature -0.0002 0.0129 -0.0256 0.0251 0.9850
ed_avg_numeric 0.0010 0.0017 -0.0023 0.0044 0.5365
age -0.0012 0.0002 -0.0016 -0.0008 0.0000
premature:ed_avg_numeric -0.0025 0.0036 -0.0097 0.0046 0.4930

destrieux_g.front.middle
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.0574 0.0371 2.9847 3.1301 0.0000
race_ethnicityBlack 0.0389 0.0136 0.0122 0.0656 0.0044
race_ethnicityHispanic 0.0167 0.0135 -0.0099 0.0432 0.2185
race_ethnicityOther 0.0296 0.0135 0.0032 0.0560 0.0277
race_ethnicityWhite 0.0330 0.0131 0.0074 0.0585 0.0116
femaleyes 0.0313 0.0026 0.0262 0.0364 0.0000
premature -0.0126 0.0111 -0.0344 0.0092 0.2565
ed_avg_numeric -0.0016 0.0015 -0.0044 0.0013 0.2817
age -0.0010 0.0002 -0.0014 -0.0007 0.0000
premature:ed_avg_numeric -0.0002 0.0031 -0.0063 0.0060 0.9601

destrieux_g.pariet.inf.supramar
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.0306 0.0458 2.9408 3.1203 0.0000
race_ethnicityBlack 0.0268 0.0141 -0.0008 0.0544 0.0575
race_ethnicityHispanic 0.0450 0.0140 0.0177 0.0724 0.0013
race_ethnicityOther 0.0395 0.0139 0.0123 0.0667 0.0045
race_ethnicityWhite 0.0582 0.0135 0.0318 0.0846 0.0000
femaleyes 0.0306 0.0027 0.0254 0.0358 0.0000
premature 0.0017 0.0115 -0.0209 0.0242 0.8849
ed_avg_numeric 0.0032 0.0015 0.0002 0.0061 0.0336
age -0.0008 0.0002 -0.0011 -0.0004 0.0000
premature:ed_avg_numeric -0.0018 0.0032 -0.0081 0.0046 0.5846

destrieux_g.temporal.middle
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.3507 0.0451 3.2623 3.4391 0.0000
race_ethnicityBlack 0.0249 0.0161 -0.0068 0.0565 0.1233
race_ethnicityHispanic 0.0630 0.0160 0.0316 0.0944 0.0001
race_ethnicityOther 0.0637 0.0159 0.0325 0.0949 0.0001
race_ethnicityWhite 0.0898 0.0155 0.0595 0.1201 0.0000
femaleyes 0.0154 0.0031 0.0094 0.0213 0.0000
premature -0.0161 0.0132 -0.0420 0.0097 0.2207
ed_avg_numeric 0.0061 0.0017 0.0028 0.0095 0.0004
age -0.0007 0.0002 -0.0012 -0.0003 0.0003
premature:ed_avg_numeric 0.0012 0.0037 -0.0061 0.0084 0.7538

destrieux_g.temp.sup.lateral
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.2483 0.0446 3.1609 3.3358 0.0000
race_ethnicityBlack 0.0614 0.0180 0.0261 0.0968 0.0007
race_ethnicityHispanic 0.0447 0.0179 0.0096 0.0797 0.0126
race_ethnicityOther 0.0595 0.0178 0.0246 0.0944 0.0008
race_ethnicityWhite 0.0768 0.0173 0.0430 0.1106 0.0000
femaleyes 0.0357 0.0034 0.0290 0.0424 0.0000
premature 0.0331 0.0147 0.0043 0.0620 0.0244
ed_avg_numeric 0.0106 0.0019 0.0068 0.0144 0.0000
age 0.0000 0.0002 -0.0004 0.0005 0.9088
premature:ed_avg_numeric -0.0071 0.0042 -0.0152 0.0011 0.0892

destrieux_s.temporal.transverse
Estimate Std. Error 95%L 95%U p-val
(Intercept) 2.8004 0.0817 2.6403 2.9605 0.0005
race_ethnicityBlack -0.0335 0.0246 -0.0817 0.0147 0.1732
race_ethnicityHispanic -0.0147 0.0244 -0.0626 0.0332 0.5469
race_ethnicityOther -0.0062 0.0243 -0.0539 0.0414 0.7980
race_ethnicityWhite 0.0049 0.0235 -0.0412 0.0511 0.8346
femaleyes 0.0715 0.0047 0.0623 0.0807 0.0000
premature 0.0189 0.0202 -0.0207 0.0584 0.3496
ed_avg_numeric 0.0047 0.0026 -0.0005 0.0098 0.0749
age -0.0012 0.0003 -0.0018 -0.0006 0.0002
premature:ed_avg_numeric -0.0069 0.0057 -0.0181 0.0042 0.2233

Income to Needs

Distribution of income-to-needs by status

destrieux_g.front.inf.opercular
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.1334 0.0285 3.0776 3.1893 0.0000
race_ethnicityBlack 0.0622 0.0135 0.0357 0.0886 0.0000
race_ethnicityHispanic 0.0183 0.0134 -0.0080 0.0445 0.1730
race_ethnicityOther 0.0297 0.0133 0.0036 0.0559 0.0259
race_ethnicityWhite 0.0292 0.0129 0.0038 0.0546 0.0241
femaleyes 0.0217 0.0026 0.0166 0.0267 0.0000
premature -0.0003 0.0064 -0.0129 0.0123 0.9617
inc2Need 0.0003 0.0007 -0.0011 0.0016 0.7172
age -0.0006 0.0002 -0.0010 -0.0003 0.0002
premature:inc2Need 0.0001 0.0015 -0.0028 0.0029 0.9675

destrieux_g.front.inf.orbital
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.2821 0.0427 3.1984 3.3659 0.0000
race_ethnicityBlack 0.0337 0.0198 -0.0050 0.0725 0.0878
race_ethnicityHispanic 0.0315 0.0196 -0.0069 0.0699 0.1084
race_ethnicityOther 0.0461 0.0195 0.0078 0.0844 0.0184
race_ethnicityWhite 0.0614 0.0189 0.0243 0.0986 0.0012
femaleyes 0.0278 0.0038 0.0204 0.0351 0.0000
premature -0.0072 0.0094 -0.0256 0.0112 0.4440
inc2Need 0.0014 0.0010 -0.0006 0.0034 0.1791
age -0.0013 0.0003 -0.0018 -0.0008 0.0000
premature:inc2Need -0.0024 0.0022 -0.0066 0.0018 0.2694

destrieux_g.front.inf.triangul
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.1247 0.0371 3.0520 3.1973 0.0000
race_ethnicityBlack 0.0324 0.0158 0.0014 0.0634 0.0402
race_ethnicityHispanic 0.0160 0.0157 -0.0147 0.0468 0.3067
race_ethnicityOther 0.0264 0.0156 -0.0042 0.0570 0.0911
race_ethnicityWhite 0.0330 0.0152 0.0033 0.0627 0.0297
femaleyes 0.0320 0.0030 0.0261 0.0379 0.0000
premature -0.0029 0.0075 -0.0176 0.0118 0.7027
inc2Need 0.0000 0.0008 -0.0016 0.0017 0.9562
age -0.0012 0.0002 -0.0016 -0.0008 0.0000
premature:inc2Need -0.0016 0.0017 -0.0050 0.0018 0.3534

destrieux_g.front.middle
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.0546 0.0367 2.9826 3.1265 0.0000
race_ethnicityBlack 0.0379 0.0136 0.0113 0.0646 0.0053
race_ethnicityHispanic 0.0161 0.0135 -0.0103 0.0426 0.2327
race_ethnicityOther 0.0293 0.0134 0.0029 0.0556 0.0294
race_ethnicityWhite 0.0332 0.0131 0.0076 0.0588 0.0110
femaleyes 0.0313 0.0026 0.0262 0.0364 0.0000
premature -0.0069 0.0065 -0.0195 0.0058 0.2865
inc2Need -0.0011 0.0007 -0.0025 0.0003 0.1410
age -0.0010 0.0002 -0.0013 -0.0007 0.0000
premature:inc2Need -0.0017 0.0015 -0.0046 0.0012 0.2632

destrieux_g.pariet.inf.supramar
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.0417 0.0455 2.9526 3.1308 0.0000
race_ethnicityBlack 0.0233 0.0141 -0.0043 0.0508 0.0976
race_ethnicityHispanic 0.0424 0.0139 0.0151 0.0697 0.0024
race_ethnicityOther 0.0381 0.0139 0.0108 0.0653 0.0061
race_ethnicityWhite 0.0575 0.0135 0.0311 0.0840 0.0000
femaleyes 0.0306 0.0027 0.0254 0.0359 0.0000
premature -0.0024 0.0067 -0.0155 0.0106 0.7149
inc2Need 0.0004 0.0007 -0.0010 0.0019 0.5455
age -0.0008 0.0002 -0.0011 -0.0004 0.0000
premature:inc2Need -0.0006 0.0015 -0.0036 0.0024 0.6959

destrieux_g.temporal.middle
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.3672 0.0447 3.2795 3.4548 0.0000
race_ethnicityBlack 0.0208 0.0161 -0.0107 0.0524 0.1959
race_ethnicityHispanic 0.0596 0.0160 0.0282 0.0909 0.0002
race_ethnicityOther 0.0621 0.0159 0.0309 0.0934 0.0001
race_ethnicityWhite 0.0891 0.0155 0.0588 0.1194 0.0000
femaleyes 0.0154 0.0031 0.0094 0.0214 0.0000
premature -0.0096 0.0076 -0.0246 0.0054 0.2105
inc2Need 0.0020 0.0008 0.0004 0.0037 0.0169
age -0.0008 0.0002 -0.0012 -0.0004 0.0003
premature:inc2Need -0.0009 0.0018 -0.0044 0.0025 0.6013

destrieux_g.temp.sup.lateral
Estimate Std. Error 95%L 95%U p-val
(Intercept) 3.2767 0.0441 3.1902 3.3632 0.0000
race_ethnicityBlack 0.0549 0.0180 0.0196 0.0902 0.0023
race_ethnicityHispanic 0.0395 0.0179 0.0045 0.0745 0.0271
race_ethnicityOther 0.0571 0.0178 0.0222 0.0920 0.0013
race_ethnicityWhite 0.0755 0.0173 0.0417 0.1094 0.0000
femaleyes 0.0357 0.0034 0.0290 0.0424 0.0000
premature 0.0232 0.0085 0.0065 0.0400 0.0065
inc2Need 0.0035 0.0009 0.0016 0.0053 0.0003
age 0.0000 0.0002 -0.0004 0.0005 0.9735
premature:inc2Need -0.0041 0.0020 -0.0079 -0.0002 0.0379

destrieux_s.temporal.transverse
Estimate Std. Error 95%L 95%U p-val
(Intercept) 2.8052 0.0811 2.6462 2.9643 0.0006
race_ethnicityBlack -0.0328 0.0246 -0.0809 0.0153 0.1815
race_ethnicityHispanic -0.0143 0.0244 -0.0622 0.0335 0.5564
race_ethnicityOther -0.0054 0.0243 -0.0531 0.0422 0.8236
race_ethnicityWhite 0.0055 0.0235 -0.0406 0.0517 0.8137
femaleyes 0.0715 0.0047 0.0623 0.0807 0.0000
premature 0.0195 0.0117 -0.0035 0.0424 0.0962
inc2Need 0.0031 0.0013 0.0005 0.0056 0.0171
age -0.0012 0.0003 -0.0018 -0.0006 0.0002
premature:inc2Need -0.0066 0.0027 -0.0119 -0.0014 0.0136